인공 지능 플랫폼을 이용한 후성유전학적 변형의 인실리코 예측 및 통합 분석 = Artificial Intelligence Platform for In Silico Prediction and Integrative Analysis of Epigenetic Modifications

다국어 초록 (Multilingual Abstract)

The analysis of persistent phenotypic changes that do not entail alterations to the DNA sequence is recognized as epigenetics. genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. Furthermore, new research has linked the aberrant state of methylation to human cancer and other diseases. Precise information about alteration locations in a genome may help us comprehend epigenetic patterns and a variety of biological roles.
Several studies have demonstrated the feasibility of discovering such locations using experimental approaches like as high throughput sequencing technology, however, the techniques are arduous (requiring significant work and time) and expensive. Due to these limitations of experimental techniques, in silico techniques for methylation identification have emerged as a viable option.
Artificial intelligence-based models are considered to be promising in silico techniques and therefore numerous models are investigated in the literature. However, these models have several research gaps which include low performance, and a small training dataset resulting in generalizability deficiency. This dissertation proposes multiple computational approaches for the prediction and integrative analysis of epigenetic modifications. We developed efficient tools for analyzing different types of DNA/RNA methylation. Moreover for every methylation different species are taken into account. Where ever necessary, we have developed new or modified datasets to improve the efficacy of in silico techniques and open doors to set new trends in the field. Regarding DNA methylation tools are proposed for identifying DNA N6-methyladenine
sites, N4-methylcytosine sites and CpG sites. While concerning RNA methylation, tools for the identification of 6-methyladenosine sites and N5-methylcytosine sites are presented. Machine learning approaches are used for N5-methylcytosine sites and CpG sites. Whereas deep neural networks are used for N6-methyladenine
sites, N4-methylcytosine sites, 6-methyladenosine sites and N5-methylcytosine sites. The predicted findings of the proposed models were more positive and outstanding than previous techniques in the literature. It is so strongly anticipated that the created approach will be more beneficial and efficient for biological research and the biopharmaceutical sector in drug design.
In summary, this thesis presents several machine learning and deep neural network-based models for analyzing different types of DNA/RNA methylation data and providing a platform for exploring the direct linkage between methylation and diseases by comprehending the complicated biological mechanisms
that enable methylation.

목차 (Table of Contents)

• 1 Introduction 1
• 1.1 Epigenetic Modifications 1
• 1.2 DNA/RNA Methylation 3
• 1.3 Problems Associated with Identifying Methylation Sites 5
• 1.4 Contribution 7
• 1.5 Thesis Outline 9
• 2 Artificial Intelligence for Computational Biology 11
• 2.1 Machine Learning 13
• 2.2 Deep Neural Networks 17
• 2.2.1 Multilayer Perceptron 20
• 2.2.2 Feed-Forward and back Propagation 20
• 2.2.3 Convolutional Neural Networks 22
• 2.2.4 Recurrent Neural Networks 25
• 2.3 Performance Metrics in Machine Learning 26
• 3 Identification of DNA N6-methyladenine Sites 30
• 3.1 Introduction 30
• 3.2 DNA6mA-MINT 32
• 3.2.1 Dataset 34
• 3.2.2 Tool Framework 34
• 3.2.3 Results and Discussion 38
• 3.3 i6mA-Caps 42
• 3.3.1 Dataset 44
• 3.3.2 Construction of i6mA-Caps 45
• 3.3.2.1 Sequence Encoding 46
• 3.3.2.2 Neural Network Architecture 47
• 3.3.2.3 Parameter Optimization 51
• 3.3.3 Results and Discussion 52
• 3.3.4 Ablation Study 52
• 3.3.5 Analysis of the i6mA-Caps tool 52
• 3.3.6 Performance comparison of i6mA-Caps with existing techniques 54
• 4 Identification of DNA N4-methylcytosine Sites 58
• 4.1 Introduction 58
• 4.2 iRG-4mC 60
• 4.2.1 Dataset 61
• 4.2.2 Tool Framework 62
• 4.2.3 Results and Discussion 66
• 4.3 DCNN-4mC Tool 68
• 4.3.1 Dataset Preparation 70
• 4.3.2 Framework of DCNN-4mC 71
• 4.3.2.1 Sequence Encoding 73
• 4.3.2.2 CNN Model 75
• 4.3.2.3 CNN model utilization for different datasets 78
• 4.3.3 Results and Analysis 79
• 4.3.3.1 Performance evaluation on Updated datasets 81
• 4.3.3.2 Cross-species validation 83
• 5 Identification of DNA CpG Sites 86
• 5.1 Introduction 86
• 5.2 iCpG-Pos 88
• 5.2.1 Dataset 90
• 5.2.2 Proposed Framework 91
• 5.2.2.1 Feature Extraction 91
• 5.2.2.1.1 OneHot Encoding 92
• 5.2.2.1.2 n-gram 93
• 5.2.2.1.3 Positional features 93
• 5.2.3 iCpG-Pos Development 98
• 5.2.4 Results and Analysis 100
• 5.2.4.1 Evaluation of Different Features and Classifiers 100
• 5.2.4.2 Comparison of iCpG-Pos with existing techniques 101
• 6 Identification of RNA N6-Methyladenosine Sites 105
• 6.1 Introduction 105
• 6.2 m6A-NeuralTool 107
• 6.2.1 Dataset 109
• 6.2.2 Framework of m6A-NeuralTool 109
• 6.2.3 Result and Analysis 114
• 6.3 DL-m6A 121
• 6.3.1 Dataset Preparation 123
• 6.3.1.1 The tissue-specific dataset 124
• 6.3.1.2 The miCLIP-Seq dataset 124
• 6.3.1.3 The m6A-Seq dataset 125
• 6.3.2 DL-m6A Framework 125
• 6.3.2.1 Sequence Encoding 126
• 6.3.2.1.1 One-hot Encoding 126
• 6.3.2.1.2 Nucleotide Chemical Property (NCP) and Nucleotide Density (ND) 127
• 6.3.2.1.3 Electron-ion interaction potential (EIIP) 128
• 6.3.3 Result and Analysis 131
• 6.3.4 Ablation Study 131
• 6.3.5 DL-m6A Performance on Tissue Specific Dataset 131
• 6.3.6 DL-m6A Performance on miCLIP-Seq 132
• 6.3.7 DL-m6A Performance on m6A-Seq Dataset 134
• 6.3.8 Performance Analysis on Cross Data 135
• 7 Identification of RNA 5-methylcytosine Sites 137
• 7.1 Introduction 137
• 7.2 im5C Tool 139
• 7.2.1 Dataset 140
• 7.2.2 im5C Framework 141
• 7.2.3 Result and Analysis 143
• 7.3 m5C-finder Tool 146
• 7.3.1 Dataset 146
• 7.3.2 m5C-finder Framework 147
• 7.3.2.1 Machine Learning Classifiers 149
• 7.3.2.1.1 Logisitic Regression (LR) 149
• 7.3.2.1.2 Random Forest (RF) 150
• 7.3.2.1.3 Support Vector Machines (SVM) 151
• 7.3.2.2 OPTUNA Optimizer 152
• 7.4 Results and Analysis 153
• 8 Conclusion and Future Research 156

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